A Comparison Study on Two Multi-scale Shape Matching Schemes

2008 ◽  
pp. 440-449 ◽  
Author(s):  
Bo Li ◽  
Henry Johan
Keyword(s):  
Shape Matching ◽  
Comparison Study ◽  
Multi Scale ◽  
Matching Schemes

Shape matching under affine transformation using normalization and multi-scale area integral features

2016 ◽  
Author(s):  
Huiying Cai ◽  
Feng Zhu ◽  
Yingming Hao ◽  
Rongrong Lu
Keyword(s):  
Affine Transformation ◽  
Shape Matching ◽  
Area Integral ◽  
Multi Scale

scholarly journals BUILDINGS CHANGE DETECTION BASED ON SHAPE MATCHING FOR MULTI-RESOLUTION REMOTE SENSING IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 683-687 ◽  
Author(s):  
M. Abdessetar ◽  
Y. Zhong
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Urban Area ◽  
Damage Assessment ◽  
Euclidean Distance ◽  
Shape Matching ◽  
Satellite Images ◽  
Remote Sensing Imagery ◽  
Multi Scale ◽  
Evolution Study

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object’s shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T<sub>0</sub> to shape T<sub>1</sub> and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).

scholarly journals Analysis of Water Bodies under Partial Cloud Conditions Using Satellite Images

2019 ◽  
Author(s):  
Rahul Neware ◽  
Mansi Thakare
Keyword(s):  
Remote Sensing ◽  
Shape Matching ◽  
Satellite Images ◽  
Feature Matching ◽  
Water Bodies ◽  
Vector Data ◽  
Water Reservoirs ◽  
Multi Scale ◽  
Water Surfaces ◽  
Complex Polygon

The technique of obtaining information or data about any feature or object from afar, called in technical parlance as remote sensing, has proven extremely useful in diverse fields. In the ecological sphere, especially, remote sensing has enabled collection of data or information about large swaths of areas or landscapes. Even then, in remote sensing the task of identifying and monitoring of different water reservoirs has proved a tough one. This is mainly because getting correct appraisals about the spread and boundaries of the area under study and the contours of any water surfaces lodged therein becomes a factor of utmost importance. Identification of water reservoirs is rendered even tougher because of presence of cloud in satellite images, which becomes the largest source of error in identification of water surfaces. To overcome this glitch, the method of the shape matching approach for analysis of cloudy images in reference to cloud-free images of water surfaces with the help of vector data processing, is recommended. It includes the database of water bodies in vector format, which is a complex polygon structure. This analysis highlights three steps: First, the creation of vector database for the analysis; second, simplification of multi-scale vector polygon features; and third, the matching of reference and target water bodies database within defined distance tolerance. This feature matching approach provides matching of one to many and many to many features. It also gives the corrected images that are free of clouds.

scholarly journals Shape Matching Based on Multi-Scale Invariant Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 115637-115649 ◽  
Author(s):  
Zhang Kun ◽  
Ma Xiao ◽  
Li Xinguo
Keyword(s):  
Shape Matching ◽  
Scale Invariant ◽  
Multi Scale ◽  
Invariant Features

scholarly journals A Comparative Study of Machine Learning Methods for Persistence Diagrams

2021 ◽  
Vol 4 ◽  
Author(s):  
Danielle Barnes ◽  
Luis Polanco ◽  
Jose A. Perea
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Shape Matching ◽  
Data Sets ◽  
Learning Methods ◽  
Data Set ◽  
Multi Scale ◽  
Machine Learning Methods ◽  
Real World Applications ◽  
Persistence Diagrams

Many and varied methods currently exist for featurization, which is the process of mapping persistence diagrams to Euclidean space, with the goal of maximally preserving structure. However, and to our knowledge, there are presently no methodical comparisons of existing approaches, nor a standardized collection of test data sets. This paper provides a comparative study of several such methods. In particular, we review, evaluate, and compare the stable multi-scale kernel, persistence landscapes, persistence images, the ring of algebraic functions, template functions, and adaptive template systems. Using these approaches for feature extraction, we apply and compare popular machine learning methods on five data sets: MNIST, Shape retrieval of non-rigid 3D Human Models (SHREC14), extracts from the Protein Classification Benchmark Collection (Protein), MPEG7 shape matching, and HAM10000 skin lesion data set. These data sets are commonly used in the above methods for featurization, and we use them to evaluate predictive utility in real-world applications.

Weighted image fusion based on multi-scale top-hat transform: Algorithms and a comparison study

Optik ◽  
2013 ◽  
Vol 124 (13) ◽  
pp. 1660-1668 ◽  
Author(s):  
Xiangzhi Bai ◽  
Shuhang Gu ◽  
Fugen Zhou ◽  
Bindang Xue
Keyword(s):  
Image Fusion ◽  
Comparison Study ◽  
Weighted Image ◽  
Multi Scale
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